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Prediction Of Crop Yields Across Four Climate Zones In Germany: An Artificial Neural Network Approach

Listed author(s):
  • Thomas Heinzow
  • Richard S.J. Tol


    (Economic and Social Research Institute, Dublin)

This paper shows the ability of artificial neural network technology to be used for the approximation and prediction of crop yields at rural district and federal state scales in different climate zones based on reported daily weather data. The method may later be used to construct regional time series of agricultural output under climate change, based on the highly resolved output of the global circulation models and regional models. Three 30-year combined historical data sets of rural district yields (oats, spring barley and silage maize), daily temperatures (mean, maximum, dewpoint) and precipitation were constructed. They were used with artificial neural network technology to investigate, simulate and predict historical time series of crop yields in four climate zones of Germany. Final neural networks, trained with data sets of three climate zones and tested against an independent northern zone, have high predictive power (0.83

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Paper provided by Research unit Sustainability and Global Change, Hamburg University in its series Working Papers with number FNU-34.

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Length: 34 pages
Date of creation: Sep 2003
Date of revision: Sep 2003
Handle: RePEc:sgc:wpaper:34
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